SubChlo: Predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm

The chloroplast is a type of plant specific subcellular organelle. It is of central importance in several biological processes like photosynthesis and amino acid biosynthesis. Thus, understanding the function of chloroplast proteins is of significant value. Since the function of chloroplast proteins correlates with their subchloroplast locations, the knowledge of their subchloroplast locations can be very helpful in understanding their role in the biological processes. In the current paper, by introducing the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm, we developed a method for predicting the protein subchloroplast locations. This is the first algorithm for predicting the protein subchloroplast locations. We have implemented our algorithm as an online service, SubChlo (http://bioinfo.au.tsinghua.edu.cn/subchlo). This service may be useful to the chloroplast proteome research.     

MicroRNA identification based on sequence and structure alignment

MOTIVATION: MicroRNAs (miRNA) are approximately 22 nt long non-coding RNAs that are derived from larger hairpin RNA precursors and play important regulatory roles in both animals and plants. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods to finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure rather than in primary sequences. Therefore, secondary structural features should be more fully exploited in the homologue search for new miRNA genes. RESULTS: In this paper, we present a novel genome-wide computational approach to detect miRNAs in animals based on both sequence and structure alignment. Experiments show this approach has higher sensitivity and comparable specificity than other reported homologue searching methods. We applied this method on Anopheles gambiae and detected 59 new miRNA genes. AVAILABILITY: This program is available at http://bioinfo.au.tsinghua.edu.cn/miralign. SUPPLEMENTARY INFORMATION: Supplementary information is available at http://bioinfo.au.tsinghua.edu.cn/miralign/supplementary.htm.     

HMMGEP: clustering gene expression data using hidden Markov models

SUMMARY: The package HMMGEP performs cluster analysis on gene expression data using hidden Markov models. AVAILABILITY: HMMGEP, including the source code, documentation and sample data files, is available at http://www.bioinfo.tsinghua.edu.cn:8080/~rich/hmmgep_download/index.html.     

Predicting subcellular localization with AdaBoost Learner

Protein subcellular localization, which tells where a protein resides in a cell, is an important characteristic of a protein, and relates closely to the function of proteins. The prediction of their subcellular localization plays an important role in the prediction of protein function, genome annotation and drug design. Therefore, it is an important and challenging role to predict subcellular localization using bio-informatics approach. In this paper, a robust predictor, AdaBoost Learner is introduced to predict protein subcellular localization based on its amino acid composition. Jackknife cross-validation and independent dataset test were used to demonstrate that Adaboost is a robust and efficient model in predicting protein subcellular localization. As a result, the correct prediction rates were 74.98% and 80.12% for the Jackknife test and independent dataset test respectively, which are higher than using other existing predictors. An online server for predicting subcellular localization of proteins based on AdaBoost classifier was available on http://chemdata.shu. edu.cn/sl12.     

Prediction of kinase-specific phosphorylation sites with sequence features by a log-odds ratio approach

Protein phosphorylation plays important roles in a variety of cellular processes. Detecting possible phosphorylation sites and their corresponding protein kinases is crucial for studying the function of many proteins. This article presents a new prediction system, called PhoScan, to predict phosphorylation sites in a kinase-family-specific way. Common phosphorylation features and kinase-specific features are extracted from substrate sequences of different protein kinases based on the analysis of published experiments, and a scoring system is developed for evaluating the possibility that a peptide can be phosphorylated by the protein kinase at the specific site in its sequence context. PhoScan can achieve a specificity of above 90% with sensitivity around 90% at kinase-family level on the data experimented. The system is applied on a set of human proteins collected from Swiss-Prot and sets of putative phosphorylation sites are predicted for protein kinase A, cyclin-dependent kinase, and casein kinase 2 families. PhoScan is available at http://bioinfo.au.tsinghua.edu.cn/phoscan/.     

Multi Label Learning for Prediction of Human Protein Subcellular Localizations

Predicting protein subcellular locations has attracted much attention in the past decade. However, one of the most challenging problems is that many proteins were found simultaneously existing in, or moving between, two or more different cell components in a eukaryotic cell. Seldom previous predictors were able to deal with such multiplex proteins although they have extremely important implications in future drug discovery in terms of their specific subcellular targeting. Approximately 20% of the human proteome consists of such multiplex proteins with multiple sample labels. In order to efficiently handle such multiplex human proteins, we have developed a novel multi-label (ML) learning and prediction framework called ML-PLoc, which decomposes the multi-label prediction problem into multiple independent binary classification problems. ML-PLoc is constructed based on support vector machine (SVM) and sequential evolution information. Experimental results show that ML-PLoc can achieve an overall accuracy 64.6% and recall ratio 67.2% on a benchmark dataset consisting of 14 human subcellular locations, and is very powerful for dealing with multiplex proteins. The current approach represents a new strategy to deal with the multi-label biological problems. ML-PLoc software is freely available for academic use at: http://www.csbio.sjtu.edu.cn/bioinf/ML-PLoc.     

iLoc-Gpos: a multi-layer classifier for predicting the subcellular localization of singleplex and multiplex Gram-positive bacterial proteins

By introducing the "multi-layer scale", as well as hybridizing the information of gene ontology and the sequential evolution information, a novel predictor, called iLoc-Gpos, has been developed for predicting the subcellular localization of Gram positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Gpos-mPLoc was adopted to demonstrate the power of iLoc-Gpos. The dataset contains 519 Gram-positive bacterial proteins classified into the following four subcellular locations: (1) cell membrane, (2) cell wall, (3) cytoplasm, and (4) extracell; none of proteins included has ≥25% pairwise sequence identity to any other in a same subset (subcellular location). The overall success rate by jackknife test on such a stringent benchmark dataset by iLoc-Gpos was over 93%, which is about 11% higher than that by GposmPLoc. As a user-friendly web-server, iLoc-Gpos is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc- Gpos or http://www.jci-bioinfo.cn/iLoc-Gpos. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user ? s convenience, the iLoc-Gpos web-server also has the function to accept the batch job submission, which is not available in the existing version of Gpos-mPLoc web-server.     

Mining gene expression data using a novel approach based on hidden Markov models

In this work we have developed a new framework for microarray gene expression data analysis. This framework is based on hidden Markov models. We have benchmarked the performance of this probability model-based clustering algorithm on several gene expression datasets for which external evaluation criteria were available. The results showed that this approach could produce clusters of quality comparable to two prevalent clustering algorithms, but with the major advantage of determining the number of clusters. We have also applied this algorithm to analyze published data of yeast cell cycle gene expression and found it able to successfully dig out biologically meaningful gene groups. In addition, this algorithm can also find correlation between different functional groups and distinguish between function genes and regulation genes, which is helpful to construct a network describing particular biological associations. Currently, this method is limited to time series data. Supplementary materials are available at http://www.bioinfo.tsinghua.edu.cn/~rich/hmmgep_supp/.     

iDNA-Prot: identification of DNA binding proteins using random forest with grey model

DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power. By incorporating the features into the general form of pseudo amino acid composition that were extracted from protein sequences via the "grey model" and by adopting the random forest operation engine, we proposed a new predictor, called iDNA-Prot, for identifying uncharacterized proteins as DNA-binding proteins or non-DNA binding proteins based on their amino acid sequences information alone. The overall success rate by iDNA-Prot was 83.96% that was obtained via jackknife tests on a newly constructed stringent benchmark dataset in which none of the proteins included has ≥25% pairwise sequence identity to any other in a same subset. In addition to achieving high success rate, the computational time for iDNA-Prot is remarkably shorter in comparison with the relevant existing predictors. Hence it is anticipated that iDNA-Prot may become a useful high throughput tool for large-scale analysis of DNA-binding proteins. As a user-friendly web-server, iDNA-Prot is freely accessible to the public at the web-site on http://icpr.jci.edu.cn/bioinfo/iDNA-Prot or http://www.jci-bioinfo.cn/iDNA-Prot. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results.     

A multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteins

Subcellular locations of proteins are important functional attributes. An effective and efficient subcellular localization predictor is necessary for rapidly and reliably annotating subcellular locations of proteins. Most of existing subcellular localization methods are only used to deal with single-location proteins. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. To better reflect characteristics of multiplex proteins, it is highly desired to develop new methods for dealing with them. In this paper, a new predictor, called Euk-ECC-mPLoc, by introducing a powerful multi-label learning approach which exploits correlations between subcellular locations and hybridizing gene ontology with dipeptide composition information, has been developed that can be used to deal with systems containing both singleplex and multiplex eukaryotic proteins. It can be utilized to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centrosome, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome, (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole. Experimental results on a stringent benchmark dataset of eukaryotic proteins by jackknife cross validation test show that the average success rate and overall success rate obtained by Euk-ECC-mPLoc were 69.70% and 81.54%, respectively, indicating that our approach is quite promising. Particularly, the success rates achieved by Euk-ECC-mPLoc for small subsets were remarkably improved, indicating that it holds a high potential for simulating the development of the area. As a user-friendly web-server, Euk-ECC-mPLoc is freely accessible to the public at the website http://levis.tongji.edu.cn:8080/bioinfo/Euk-ECC-mPLoc/. We believe that Euk-ECC-mPLoc may become a useful high-throughput tool, or at least play a complementary role to the existing predictors in identifying subcellular locations of eukaryotic proteins.     

ATID: a web-oriented database for collection of publicly available alternative translational initiation events

SUMMARY: Alternative translational initiation is an important cellular mechanism contributing to the diversity of protein products and functions. We develop a database that provides a comprehensive collection of alternative translational initiation events. The purpose of this alternative translational initiation database (ATID) is to facilitate the systematic study of alternative translational initiation of genes. The current version of database contains 300 genes from Homo sapiens, Mus musculus and other species. Each of the genes has two or more isoforms due to alternative translational initiation. Resources in ATID, including gene information, alternative products of genes and domain structures of isoforms, are provided through a user-friendly web interface. AVAILABILITY: The ATID database is available for public use at http://bioinfo.au.tsinghua.edu.cn/atie/.     

PBEAM: A parallel implementation of BEAM for genome-wide inference of epistatic interactions

The software tool PBEAM provides a parallel implementation of the BEAM, which is the first algorithm for large scale epistatic interaction mapping, including genome-wide studies with hundreds of thousands of markers. BEAM describes markers and their interactions with a Bayesian partitioning model and computes the posterior probability of each marker sets via Markov Chain Monte Carlo (MCMC). PBEAM takes the advantage of simulating multiple Markov chains simultaneously. This design can efficiently reduce ~n-fold execution time in the circumstance of n CPUs. The implementation of PBEAM is based on MPI libraries.

Availability

PBEAM is available for download at http://bioinfo.au.tsinghua.edu.cn/pbeam/     

OP-Synthetic: identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

Constraint-based flux analysis has been widely used in metabolic engineering to predict genetic optimization strategies. These methods seek to find genetic manipulations that maximally couple the desired metabolites with the cellular growth objective. However, such framework does not work well for overproducing chemicals that are not closely correlated with biomass, for example non-native biochemical production by introducing synthetic pathways into heterologous host cells. Here, we present a computational method called OP-Synthetic, which can identify effective manipulations (upregulation, downregulation and deletion of reactions) and produce a step-by-step optimization strategy for the overproduction of indigenous and non-native chemicals. We compared OP-Synthetic with several state-of-the-art computational approaches on the problems of succinate overproduction and N-acetylneuraminic acid synthetic pathway optimization in Escherichia coli. OP-Synthetic showed its advantage for efficiently handling multiple steps optimization problems on genome wide metabolic networks. And more importantly, the optimization strategies predicted by OP-Synthetic have a better match with existing engineered strains, especially for the engineering of synthetic metabolic pathways for non-native chemical production. OP-Synthetic is freely available at:http://bioinfo.au.tsinghua.edu.cn/member/xwwang/OPSynthetic/.     

Prediction of C-to-U RNA editing sites in plant mitochondria using both biochemical and evolutionary information

Although cytidine-to-uridine conversions in plant mitochondria were discovered 18 years ago, it was still an enigmatic process. Since the sequencing projects of plant mitochondrial genomes are providing more and more available sequences, the requirements of computationally identifying C-to-U RNA editing sites are also increasing. By incorporating both evolutionary and biochemical information, we developed a novel algorithm for predicting C-to-U RNA editing sites in plant mitochondria. The algorithm has been implemented as an online service called CURE (Cytidine-to-Uridine Recognizing Editor). CURE performs better than other methods that are based on only biochemical or only evolutionary information. CURE also provides the ability of predicting C-to-U RNA editing sites in non-coding regions and the synonymous C-to-U RNA editing sites in coding regions that are impossible for other methods. Furthermore, CURE can carry out prediction directly on the entire mitochondria genome sequence. The prediction results of CURE suggest the functional importance of synonymous RNA editing sites, which was neglected before. The CURE service can be accessed at http://bioinfo.au.tsinghua.edu.cn/cure.     

EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression

Background

B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task.

Results

In this work, based on the antigen’s primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728.

Conclusions

We have presented a reliable method for the identification of linear B cell epitope using antigen’s primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0414-y) contains supplementary material, which is available to authorized users.     

Identify submitochondria and subchloroplast locations with pseudo amino acid composition: Approach from the strategy of discrete wavelet transform feature extraction

It is very challenging and complicated to predict protein locations at the sub-subcellular level. The key to enhancing the prediction quality for protein sub-subcellular locations is to grasp the core features of a protein that can discriminate among proteins with different subcompartment locations. In this study, a different formulation of pseudoamino acid composition by the approach of discrete wavelet transform feature extraction was developed to predict submitochondria and subchloroplast locations. As a result of jackknife cross-validation, with our method, it can efficiently distinguish mitochondrial proteins from chloroplast proteins with total accuracy of 98.8% and obtained a promising total accuracy of 93.38% for predicting submitochondria locations. Especially the predictive accuracy for mitochondrial outer membrane and chloroplast thylakoid lumen were 82.93% and 82.22%, respectively, showing an improvement of 4.88% and 27.22% when other existing methods were compared. The results indicated that the proposed method might be employed as a useful assistant technique for identifying sub-subcellular locations. We have implemented our algorithm as an online service called SubIdent (http://bioinfo.ncu.edu.cn/services.aspx).     

pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC

Information of the proteins' subcellular localization is crucially important for revealing their biological functions in a cell, the basic unit of life. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop computational tools for timely identifying their subcellular locations based on the sequence information alone. The current study is focused on the Gram-negative bacterial proteins. Although considerable efforts have been made in protein subcellular prediction, the problem is far from being solved yet. This is because mounting evidences have indicated that many Gram-negative bacterial proteins exist in two or more location sites. Unfortunately, most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions important for both basic research and drug design. In this study, by using the multi-label theory, we developed a new predictor called “pLoc-mGneg” for predicting the subcellular localization of Gram-negative bacterial proteins with both single and multiple locations. Rigorous cross-validation on a high quality benchmark dataset indicated that the proposed predictor is remarkably superior to “iLoc-Gneg”, the state-of-the-art predictor for the same purpose. For the convenience of most experimental scientists, a user-friendly web-server for the novel predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGneg/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.     

A novel method for protein secondary structure prediction using dual-layer SVM and profiles

A high-performance method was developed for protein secondary structure prediction based on the dual-layer support vector machine (SVM) and position-specific scoring matrices (PSSMs). SVM is a new machine learning technology that has been successfully applied in solving problems in the field of bioinformatics. The SVM's performance is usually better than that of traditional machine learning approaches. The performance was further improved by combining PSSM profiles with the SVM analysis. The PSSMs were generated from PSI-BLAST profiles, which contain important evolution information. The final prediction results were generated from the second SVM layer output. On the CB513 data set, the three-state overall per-residue accuracy, Q3, reached 75.2%, while segment overlap (SOV) accuracy increased to 80.0%. On the CB396 data set, the Q3 of our method reached 74.0% and the SOV reached 78.1%. A web server utilizing the method has been constructed and is available at http://www.bioinfo.tsinghua.edu.cn/pmsvm.     

A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites

Prediction of protein subcellular localization is a challenging problem, particularly when the system concerned contains both singleplex and multiplex proteins. In this paper, by introducing the "multi-label scale" and hybridizing the information of gene ontology with the sequential evolution information, a novel predictor called iLoc-Gneg is developed for predicting the subcellular localization of gram-positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Gneg-mPLoc was adopted to demonstrate the power of iLoc-Gneg. The dataset contains 1,392 gram-negative bacterial proteins classified into the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. Of the 1,392 proteins, 1,328 are each with only one subcellular location and the other 64 are each with two subcellular locations, but none of the proteins included has pairwise sequence identity to any other in a same subset (subcellular location). It was observed that the overall success rate by jackknife test on such a stringent benchmark dataset by iLoc-Gneg was over 91%, which is about 6% higher than that by Gneg-mPLoc. As a user-friendly web-server, iLoc-Gneg is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Gneg. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user's convenience, the iLoc-Gneg web-server also has the function to accept the batch job submission, which is not available in the existing version of Gneg-mPLoc web-server. It is anticipated that iLoc-Gneg may become a useful high throughput tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development.     

MSLoc-DT: A new method for predicting the protein subcellular location of multispecies based on decision templates

Revealing the subcellular location of newly discovered protein sequences can bring insight to their function and guide research at the cellular level. The rapidly increasing number of sequences entering the genome databanks has called for the development of automated analysis methods. Currently, most existing methods used to predict protein subcellular locations cover only one, or a very limited number of species. Therefore, it is necessary to develop reliable and effective computational approaches to further improve the performance of protein subcellular prediction and, at the same time, cover more species. The current study reports the development of a novel predictor called MSLoc-DT to predict the protein subcellular locations of human, animal, plant, bacteria, virus, fungi, and archaea by introducing a novel feature extraction approach termed Amino Acid Index Distribution (AAID) and then fusing gene ontology information, sequential evolutionary information, and sequence statistical information through four different modes of pseudo amino acid composition (PseAAC) with a decision template rule. Using the jackknife test, MSLoc-DT can achieve 86.5, 98.3, 90.3, 98.5, 95.9, 98.1, and 99.3% overall accuracy for human, animal, plant, bacteria, virus, fungi, and archaea, respectively, on seven stringent benchmark datasets. Compared with other predictors (e.g., Gpos-PLoc, Gneg-PLoc, Virus-PLoc, Plant-PLoc, Plant-mPLoc, ProLoc-Go, Hum-PLoc, GOASVM) on the gram-positive, gram-negative, virus, plant, eukaryotic, and human datasets, the new MSLoc-DT predictor is much more effective and robust. Although the MSLoc-DT predictor is designed to predict the single location of proteins, our method can be extended to multiple locations of proteins by introducing multilabel machine learning approaches, such as the support vector machine and deep learning, as substitutes for the K-nearest neighbor (KNN) method. As a user-friendly web server, MSLoc-DT is freely accessible at http://bioinfo.ibp.ac.cn/MSLOC_DT/index.html.